Data/MLOps Engineer – CT&C

We are looking for an experienced and passionate Data/MLOps Engineer to join our CT&C Engineering team. In this role, you will bridge the gap between Data Science and Production Engineering, ensuring that machine learning solutions are scalable, reliable, secure, and production-ready.

You will play a key role in designing, building, maintaining, and optimizing our data platforms and ML infrastructure, enabling efficient data ingestion, transformation, storage, model deployment, and real-time analytics.

This position requires a strong understanding of machine learning concepts, hands-on MLOps expertise, and solid engineering skills across cloud platforms, data processing frameworks, and automation tooling.

Key Responsibilities

• ML & Data Infrastructure

• Deploy, maintain, and optimize end-to-end machine learning lifecycles, including automated training, deployment, monitoring, and versioning.

• Build and support core MLOps capabilities such as Feature Stores, Experiment Tracking platforms, and Model Registries.

• Provision and manage scalable cloud infrastructure using Infrastructure as Code (IaC) solutions such as Terraform or AWS CloudFormation.

• Design and implement robust CI/CD/CT (Continuous Training) pipelines to enable reliable and repeatable production releases.

• Collaborate closely with Data Scientists to productionize machine learning models and workflows.

• Data Engineering & Pipeline Optimization

• Design and develop high-volume data ingestion and processing pipelines using Apache Spark, PySpark, and Python.

• Build scalable ETL/ELT solutions supporting advanced analytics and machine learning workloads.

• Implement optimized data models and storage strategies to support low-latency model inference and high-performance analytics.

• Integrate automated data quality validation, monitoring, and observability capabilities across data platforms.

• Governance, Monitoring & Security

• Implement proactive monitoring for model performance, model drift, data quality issues, and system latency.

• Ensure complete reproducibility through robust versioning of data, code, models, and artifacts.

• Apply security best practices across the ML lifecycle, including access management, data privacy, and compliance requirements.

• Support operational excellence through incident management, troubleshooting, and continuous improvement initiatives.

• Agile Delivery & Collaboration

• Work within Agile delivery teams, participating in sprint planning, backlog refinement, daily stand-ups, and retrospectives.

• Translate business and data science requirements into scalable technical solutions.

• Collaborate with Product Owners, Data Scientists, Data Engineers, and Platform Teams to deliver production-grade ML solutions.

• Create and maintain technical documentation covering architecture, workflows, pipelines, and operational procedures.

What We're Looking For:

• Strong Python development experience

• Hands-on experience with Apache Spark and PySpark

• Solid understanding of machine learning lifecycle management and MLOps best practices

• Experience with AWS services, particularly:

• Amazon SageMaker

• AWS Lambda

• AWS CDK

• Experience building CI/CD pipelines for data and ML workloads

• Strong SQL skills

• Experience designing and implementing ETL/ELT pipelines

• Knowledge of PyTorch and machine learning frameworks

• Experience with Infrastructure as Code (Terraform and/or CloudFormation)

• Understanding of monitoring, observability, and production support practices

• Experience working in Agile environments

• Design and implement scalable ML solutions using PySpark and Amazon SageMaker.

• Balance software engineering best practices with practical machine learning implementation.

• Drive operational excellence across the entire ML lifecycle.

• Experience with Feature Stores and Model Registry platforms

• Experience implementing Continuous Training (CT) pipelines

• Knowledge of MLOps governance frameworks

• Experience with real-time streaming architectures

• Exposure to large-scale cloud-native data platforms